Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

scholarly journals Penerapan Algoritma Decision Tree C4.5 Untuk Memprediksi Kelayakan Calon Pendonor Melakukan Donor Darah Dengan Klasifikasi Data Mining

2021 ◽  
Vol 2 (4) ◽  
pp. 181-189
Author(s):  
Yuda Irawan
Keyword(s):  
Data Mining ◽  
Blood Pressure ◽  
Information System ◽  
Body Weight ◽  
Decision Tree ◽  
Blood Donors ◽  
Blood Donation ◽  
Decision Tree Algorithm ◽  
C4.5 Algorithm ◽  
Data Mining Application

Based on data from UDD PMI Kampar Regency, many donors must have provisions to become blood donors. So far, blood donor selection has been made manually to determine whether potential donors can donate blood or not. Meanwhile, today's information system has not yet explored further information from the large amount of data stored as knowledge. There is a need for organizational consolidation and continuous evaluation of the performance that has been carried out by PMI in dealing with social and humanitarian problems. By making a data mining application with a classification method using the Decision Tree C4.5 Algorithm in predicting someone worthy or not to donate blood, it can be calculated from the results of variables that are continuous or critical, such as variables of age, body weight, hemoglobin (HB) levels, blood pressure. (systolic and diastolic), The data that enters the information system is calculated using the Decision Tree C4.5 Algorithm formula, which results in detailed results and can produce valid and more accurate values. With the data mining application using the Decision Tree Algorithm C4.5 method, potential blood donors' eligibility can be classified based on age, body weight, hemoglobin, and blood pressure. Hemoglobin with the highest gain value (0.861212618) is the variable that most determines blood donation success.

scholarly journals An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm

2018 ◽  
Vol 3 (12) ◽  
pp. 126-134
Author(s):  
Yusuf Perwej ◽  
Firoj Parwej ◽  
Nikhat Akhtar
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Decision Tree ◽  
Distance Measures ◽  
Jaccard Coefficient ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
Complex Information ◽  
C4.5 Algorithm ◽  
Enormous Number

The data mining techniques have the ability to discover hidden patterns or correlation among the objects in the medical data. There are many areas that adapt data mining techniques, namely marketing, stock, health care sector and so on. In the health care industry produces gigantic quantities of data that clutches complex information relating to the sick person and their medical conditions. The data mining has an infinite potential to make use of healthcare data more effectually and efficiently to predict various kinds of disease. The present-time healthcare industry heart ailment is a term that assigns to an enormous number of health care circumstances related to heart. These medical circumstances relate to the unexpected health circumstance that straight control the cardiac.  In this paper we are using a ROCK algorithm because it uses Jaccard coefficient on the contrary using the distance measures to find the similarity between the data or documents to classify the clusters and the contrivance for classifying the clusters based on the similarity measure shall be used over a given set of data. Afterward, C4.5 algorithm is used as the training algorithm to show the rank of a cardiac ailment with the decision tree. The C4.5 can be referred as the statistic classifier as well as this algorithm uses avail radio for feature selection and to build the decision tree. The C4.5 algorithm is widely used because of its expeditious classification and high exactitude. Lastly, the cardiac ailment database is clustered using the K-means clustering, which will alienate the data convenient to cardiac sickness from the database.

scholarly journals Pengaruh Penerapan Teknik Data Mining Dalam Menemukan Kriteria Kelayakan Penerima Bonus Tambahan Pegawai Menggunakan Algoritma C4.5

2019 ◽  
Vol 1 ◽  
pp. 788
Author(s):  
Wulan Nadia Puri Heriani ◽  
Irfan Sudahri Damanik ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Employee Performance ◽  
Data Mining Techniques ◽  
Work Location ◽  
Location Determination ◽  
C4.5 Algorithm ◽  
A Company

Employees are components where the company's future depends on how the performance and contributions are given. Employee performance can also be determined by how the company treats employees, both in terms of awards to each employee, work location determination and salary. Every employee who works in a company basically has one reason, namely getting a decent salary in accordance with his field. Here the company takes the initiative to provide additional bonuses to each decent employee. Therefore companies need to know the criteria that greatly influence the feasibility of giving bonuses so that companies can more easily draw conclusions. This research will help companies use data mining techniques with the c4.5 algorithm. Data mining is a series of processes to explore values in information that might not be known manually. C4.5 algorithm is used to make a decision tree that will display the results of the problem under study.

scholarly journals Analisis Dengan Metode Klasifikasi Menggunakan Decission Tree Untuk Memprediksi Penentuan Resiko kredit Bank

JURTEKSI ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 101-106
Author(s):  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Credit Risk ◽  
Risk Prediction ◽  
Data Mining Techniques ◽  
Analysis Process ◽  
Time And Energy ◽  
Number Of Customers ◽  
Excel Format ◽  
Existing Data

Abstract: There are several facilities in distributing funds to the customer which is owned by Bank Syariah Bukopin. One of them is Kredit Pemilikan Rumah / Housing Loan (mortgage), so far the bank when provides mortgages to customers still uses risk prediction manually in giving credit to customers which is taking up a lot of time and energy especially when the customer reports is further analyzed by the Bank. One technique that can help in predicting the Bank's credit risk determination is Decision Tree which is a technique that is a part of Data Mining techniques to take a decision in the form of a tree. With Decision Tree techniques, it is expected to help the bank to allow faster and easier in predicting the data and getting a conclusion from existing data. One of the ways to predict the data is using Dtreg software. This software only uses data that is in the format of "csv (comma delimited)�, if it is not using the format" csv (comma delimited)", so that the data can not be processed by Dtreg software. When the excel format has been converted to the "csv (comma delimited)" format, the analysis process can be done. Dtreg can generate decision tree, one of them is the result of risk decision from the number of mortgages based on the number of customers. Keywords: data mining, decision tree Abstrak: Ada beberapa fasilitas dalam penyaluran dana ke nasabah yang di miliki Bank Syariah Bukopin. Salah satunya Kredit Pemilikan Rumah (KPR), selama ini pihak Bank memberikan KPR ke nasabah masih menggunakan prediksi resiko secara manual dalam meberikan kredit kepada nasabah yang banyak menyita waktu dan tenaga apalagi pada saat laporan nasabah dianalisa lebih lanjut oleh pihak Bank. Salah satu teknik yang dapat membantu pihak Bank dalam memprediksi Penentuan resiko kredit adalah teknik Decision Tree yang merupakan bagian dari teknik Data Mining untuk mengambil suatu keputusan dalam bentuk pohon. Dengan teknik Decision Tree diharapkan dapat membantu pihak bank agar lebih cepat dan mudah dalam memprediksi data dan menarik suatu kesimpulan dari data yang ada.Salah satu cara memprediksi data tersebut dengan menggunakan software Dtreg. Pada software ini data yang digunakan hanya bisa dalam bentuk format �csv (comma delimited), jika tidak menggunakan format �csv (comma delimited)� maka data tersebut tidak bisa diproses oleh software Dtreg dan selanjutnya jika format excel yang telah dirubah ke format �csv (comma delimited)�, maka akan dapat dilakukan proses analisa. Dtreg dapat menghasilkan pohon keputusan, salah satu nya yaitu hasil keputusan resiko dari jumlah kredit pemilikan rumah berdasarkan jumlah nasabah. Kata kunci: data mining, decision tree

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

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